Message embedding and emulation in entropy encoder-decoder networks

ABSTRACT

Certain aspects of the present disclosure provide techniques and apparatus for messaging in a wireless communications system using neural networks. An example method generally includes receiving a first message and a second message, wherein the second message comprises a secret message to be hidden in the first message. The first message and second message are combined into a combined message. An emulation message is generated through an encoder neural network based on the combined message. The emulation message generally comprises a message decodable by a receiving device into the first message. The emulation message is output emulation message for transmission to the receiving device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and benefit of U.S. ProvisionalPatent Application Ser. No. 63/269,587, entitled “Message Embedding andEmulation in Entropy Encoder-Decoder Networks,” filed Mar. 18, 2022, andassigned to the assignee hereof, the entire contents of which areincorporated by reference herein.

INTRODUCTION

Aspects of the present disclosure relate to using neural networks togenerate messages in a wireless communications system.

In a wireless communications system, messaging is generally exchangedbetween different devices (e.g., network entities, such as basestations, user equipments (UEs), etc.) in order to transmit and/orreceive information. In some cases, the devices in the wirelesscommunications system may be heterogeneous in terms of supportedfunctionalities. For example, legacy devices may support a subset of thefunctionality of non-legacy devices. In another example, devices fromone manufacturer may support features that are not supported by devicesfrom other manufacturers. However, because these devices may generallysupport a common set of features, the messaging generated to communicateamongst devices in a wireless communications network may be structuredto comply with some basic defined set of parameters. For example,messaging may be established such that all devices in the wirelesscommunications system can understand, decode, and process messaging forfeatures related to the common set of features supported by all devicesin the wireless communications system.

To support features that are outside the common set of featuressupported by all devices in the wireless communications system, varioustechniques can be used. In some cases, existing messages can includereserved fields designated for use in newer generations of wirelesscommunications networks. In other cases, additional messaging may bedefined to support non-legacy features, manufacturer-specific features,or the like. However, the use of reserved fields and/or additionalmessaging may impose additional transmission and reception overhead,increase the amount of power used in transmitting and receivingmessages, and otherwise represent an inefficient use of resources in awireless communications system.

Accordingly, what is needed are improved techniques for generatingtransmitting, receiving, and decoding messaging in wirelesscommunications systems.

BRIEF SUMMARY

Certain aspects provide a method for generating messaging in a wirelesscommunications system. An example method generally includes receiving afirst message and a second message, wherein the second message comprisesa secret message to be embedded into inside the first message. The firstmessage and second message are combined into a combined message. Anemulation message is generated through an encoder neural network basedon the combined message. The emulation message generally comprises amessage decodable by a receiving device into the first message,regardless of whether the receiving device supports messaging using thesecond message included in the combined message. The emulation messageis output emulation message for transmission to a receiving device.

Certain aspects provide a method for receiving messaging in a wirelesscommunications system. An example method generally includes receiving anemulation message for processing, wherein the emulation messagecomprises a message decodable by a receiver into a first message. Theemulation message is decoded into an approximation of the first messageand an approximation of a second message through a decoder neuralnetwork. Generally, the second message comprises a secret message hiddenwithin the first message. One or more actions are taken based on theapproximation of the first message and the approximation of the secondmessage.

Certain aspects provide a method for communicating in a wirelesscommunications system. An example method generally includes combining afirst message and a second message into a combined message. An emulationmessage is generated through an encoder neural network based on thecombined message, and the emulation message generally comprises amessage decodable by a receiving device into the first message. Theemulation message is decoded into an approximation of the first messageand an approximation of the second message through a decoder neuralnetwork. One or more actions are taken based on the approximation of thefirst message and the approximation of the second message.

Other aspects provide processing systems configured to perform theaforementioned methods as well as those described herein;non-transitory, computer-readable media comprising instructions that,when executed by one or more processors of a processing system, causethe processing system to perform the aforementioned methods as well asthose described herein; a computer program product embodied on acomputer readable storage medium comprising code for performing theaforementioned methods as well as those further described herein; and aprocessing system comprising means for performing the aforementionedmethods as well as those further described herein.

The following description and the related drawings set forth in detailcertain illustrative features of one or more aspects.

BRIEF DESCRIPTION OF THE DRAWINGS

The appended figures depict certain aspects of the present disclosureand are therefore not to be considered limiting of the scope of thisdisclosure.

FIG. 1 depicts an example communications pipeline for transmitting andreceiving emulation messages generated and decoded using anencoder-decoder neural network, according to aspects of the presentdisclosure.

FIG. 2 depicts an example cross-correlation matrix illustratingcorrelated bits in a message, according to aspects of the presentdisclosure.

FIG. 3 depicts an example feedback loop for refining an encoder-decoderneural network used in generating emulation messages, according toaspects of the present disclosure.

FIG. 4 depicts example operations for generating and transmittingemulation messages in a wireless communications system using an encoderneural network, according to aspects of the present disclosure.

FIG. 5 depicts example operations for receiving and processing emulationmessages in a wireless communications system using a decoder neuralnetwork, according to aspects of the present disclosure.

FIG. 6 depicts example operations for communicating in a wirelesscommunications system via emulation messages generated by anencoder-decoder neural network, according to aspects of the presentdisclosure

FIG. 7 depicts an example message space in which an encoder-decoderneural network may be trained to generate emulation messages, accordingto aspects of the present disclosure.

FIG. 8 depicts an example implementation of a processing system on whichan encoder-decoder neural network may be used to generate, transmit,receive, and decode emulation messages for transmission in a wirelesscommunications system, according to aspects of the present disclosure.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe drawings. It is contemplated that elements and features of oneaspect of the present disclosure may be beneficially incorporated inother aspects of the present disclosure without further recitation.

DETAILED DESCRIPTION

Aspects of the present disclosure provide techniques and apparatus forencoding, transmitting, receiving, and decoding emulation messagesincluding a first message and an embedded second message (also referredto as a hidden message) in a wireless communications system via anencoder-decoder neural network. Generally, these emulation messages mayappear to be valid messages according to a defined message format (e.g.,in a wireless communications standard, such as various revisions of the802.11 Wi-Fi standard, the Long Term Evolution (LTE) standard, the NewRadio (NR) standard, or the like) but may include additional informationusable by devices that support additional functionality beyond thatdefined in any particular standard (e.g., to supportmanufacturer-specific functionality or the like).

In a wireless communications network, standardized messages havingdefined formats (e.g., acknowledgment/negative acknowledgment (ACK/NACK)messages, messages used in communicating information for initial access(random access messages) or handover from one cell in a wireless networkto another, messages used to report signal quality (e.g., channel stateinformation (CSI) messages), or the like) may be used to transmitinformation to and receive information from other devices in thewireless communications network. These standardized messages generallyinclude a defined payload according to a defined format that can beunderstood by any participant in the wireless communications network.Because messages may be standardized, any device that complies with acommunications standard defining the messaging and the associatedbehavior of transmitting and receiving devices can participate in awireless communications network. Further, to support backwardscompatibility, the defined format for these messages (e.g., specifyingdifferent groups of bits within a message) may remain static over time;new communications standards may assign different meanings to portionsof a message for non-legacy devices and may introduce new messaging, buttypically may not change meaning assigned to different portions of themessage for legacy devices.

Because message formats and payloads in wireless communications networksmay be standardized, adding new features in wireless communicationsnetworks may be achieved by using reserved fields of previously definedmessages or by defining new messaging. Using reserved fields ofpreviously defined messages may be feasible, such as when reservedfields exist in a previously defined message and are available for use(e.g., have not been used for other purposes, such as other featuresdefined in a communications standard). Further, when reserved fields areunused, resources may be wasted by transmitting null information inthese reserved fields. Adding new messaging in a wireless communicationssystem may allow for new features to be introduced (e.g.,device-manufacturer-specific features) without redefining existingmessaging. However, this may also increase the transmission overhead ina wireless communications system and may waste resources and processingpower, especially for devices that are unable to successfully decodethis new messaging.

Aspects of the present disclosure provide techniques that allow for theefficient generation, transmission, reception, and decoding of signalingin wireless communications systems using encoder-decoder neuralnetworks, with the encoder being used for the generation andtransmission of signaling and the decoder being used for processingreceived signaling. Extended messaging—representing messaging related tonew features and/or extensions to existing features,device-manufacturer-specific features, or the like—may be embeddedwithin standard messages. These standard messages may be decodable byany device in the wireless communications system. Because the extendedmessaging may be embedded within the standard messages, extendedmessaging may be transmitted in a wireless communications system withoutincurring the additional overhead of transmitting the extended messagingas standalone messages. Further, because the extended messaging isembedded within a standard message, devices that do not support thefeatures included in or otherwise associated with the extended messagingmay be unaware that such extended messaging is embedded in the standardmessage; rather, the standard message can be decoded according to thetypical rules established (e.g., by a communications standard) forprocessing such messaging. Thus, aspects of the present disclosure mayreduce transmission overhead in wireless communications system involvedin transmitting messages related to new features or non-standardfeatures, while allowing such messages to be transmitted within standardmessages that are understandable by any device in a wirelesscommunications system. Further, the embedding of extended messaging instandard messaging may reduce power utilized in transmitting andreceiving messages, as a single message may be transmitted tocommunicate standard and extended messages instead of communicatingstandard messaging and extended messaging in separate transmissions.

Example Encoder-Decoder Message Generation Pipeline

FIG. 1 illustrates an example communications pipeline 100 fortransmitting and receiving emulation messages generated and decodedusing an encoder-decoder neural network, according to aspects of thepresent disclosure.

To allow for a first message (also referred to as a standard message)and a second message (also referred to as an extended message) to betransmitted in a single message, communications pipeline 100 includes anencoder-decoder neural network (including encoder network 120 anddecoder network 140) configured to generate and decode an emulationmessage including both the first message and the second message. Theemulation message generally may be a message that, when decoded by astandard message decoder at a receiving device, results in the recoveryof the first message (or at least an approximation of the firstmessage). When decoded using a decoder neural network (e.g., the decoderportion of an encoder-decoder neural network trained to generate anddecode emulation messages), however, both an approximation of the firstmessage and an approximation of the second message may be recovered.

As illustrated, pipeline 100 includes a message combiner 110, entropyencoder network 120, physical (PHY) layer 130, entropy decoder network140, and decoder 150. Message combiner 110, entropy encoder network 120,and the forward error correction (FEC) encoder and transmit (Tx) PHYlayer components of PHY layer 130 may be located at a transmittingdevice, while the receive (Rx) PHY layer components and FEC decoder ofPHY layer 130, entropy decoder network 140, and decoder 150 may belocated at the receiving device. Generally, entropy decoder network 140may recover a first message and a second message from a received messageusing a neural network, and decoder 150 may decode the received messageinto the first message in parallel (or substantially in parallel),without using a neural network. While pipeline 100 is illustrated asincluding both entropy decoder network 140 and decoder 150, it should berecognized that decoder 150 need not be used to decode receivedmessages. It should be understood that a device may include both thetransmission and reception-side components, in order to allow such adevice both to generate and transmit an emulation message including thefirst message and second message and to decode a different first messageand second message from a received emulation message.

As illustrated and discussed in further detail herein, message combiner110 may take a first message X, a second message H, and (in someaspects) supplemental information S and generate a combined message tobe encoded by entropy encoder network 120. In some aspects, the firstmessage X may be a standard message usable by legacy devices, and thesecond message H may be an extended message having information that isusable to enable various features supported by non-legacy devices. Inanother example, the first message X may be a message formattedaccording to a wireless communications standard (e.g., defined formatsof messages in various revisions to the 802.11 Wi-Fi standard, in theLong Term Evolution (LTE) standard, the New Radio (NR) standard, etc.),and the second message H may be an extended message not defined by awireless communications standard or defined in a new wirelesscommunications standard. For example, the second message X may be adevice-manufacturer-specific message to enable manufacturer-specificfunctionality.

First message X may include i bit fields of varying levels ofstochasticity and may be represented by the equation:

${X = {\bigvee\limits_{i}x_{i}}},$

where V represents a bit concatenation operation, and x_(i)ϵ{0,1}represents that value of the i^(th) bit field. Each bit field x_(i) isstochastic in nature, as there is no general pattern as to whether a bitfield x_(i) has a value of 0 or 1. Each bit field x_(i) is alsoconditionally stochastic with other bits, as realistic bit values in amessage X for a given bit x_(i) may, in some cases, depend upon orotherwise have some relation to other bit values in the message X. Forexample, for a first field with some given value a, the range of validvalues for a second field may be b⊏B, where B represents the whole rangeof values for the related field and b represents the subset of validvalues for the second field when the first field has a value a.

In some aspects, the supplemental information S included in the combinedmessage may include various metrics that can be used by entropy encodernetwork 120 to generate the embedded message. For example, thesupplemental information may include historical signal quality orhistorical channel quality information metrics. The signal quality orchannel quality information metrics may include information such as areceived signal strength indicator (RSSI), reference signal receivedquality (RSRQ) metric, a channel quality indicator (CQI), a modulationand coding scheme (MCS), a bit error rate or block error rate, or thelike. The supplemental information may, in some aspects, be used by theentropy encoder network 120 to generate emulation messages that appearrealistic, in view of the signal quality metrics or channel qualityinformation metrics at the time at which such a message is generated.For example, the use of the supplemental information S to generate theemulation message can be used to ensure that values in some fields wouldappear to be accurate for the current state of the channel on whichthese messages are transmitted, which may further allow for theemulation message to be successfully decoded by a standard messagedecoder.

Entropy encoder network 120 generally generates an emulation message Efrom the combined message {X, H, S} generated by message combiner 110.As discussed, the entropy encoder network 120 may generate an emulationmessage E that, when decoded by decoder 150, results in the recovery ofan approximation X′ 152 of first message X and, when decoded by entropydecoder network 140, results in the recovery of an approximation X′ 142of first message X and an approximation H′ 144 of second message H.Generally, in encoding the combined message {X, H, S} into message EϵM,the combined message may be encoded into fewer bits than the totalnumber of bits of first message X and second message H based on thefield stochasticity of the first message X, in which the fields of X arerandom and may not be predictable based on the values of other fields inthe first message X.

Emulation message E may be output by entropy encoder network 120 to theFEC encoder and transmission components of the PHY layer 130 fortransmission to a receiving device. Generally, the FEC encoder may applychannel coding and a cyclic redundancy check to emulation message E toensure error-free (or error-minimized) delivery of emulation message Eto the receiving device. At the receiving device, the receiver PHY layerand FEC decoder components of the PHY layer 130 may be used to recoveran approximation E′ of emulation message E. Due to the forward errorcorrection applied to emulation message E at the transmitting device,E=E′, under a constraint of a target message block error rate.

As illustrated, E′ may be delivered to an entropy decoder network 140and to a decoder 150 for processing. Entropy decoder network 140, asdiscussed, is generally configured to recover approximations of both X′and H′ from E′, while decoder 150 is generally configured to decode E′into X″. X′ and X″ may be processed using legacy processing techniques,while H′ may be processed using non-legacy techniques. Generally, X′ andX″ may be equivalent messages, recovered from E′ using differenttechniques.

To generate emulation messages E that can be successfully decoded bydecoder 150 into first message X (or suitable approximations thereof),entropy encoder network 120 and entropy decoder network 140 may betrained to minimize, or at least reduce, a total loss that is the sum ofa carrier message loss, hidden message loss, emulation message loss, andpublic performance loss.

Carrier message loss L_(X) may be represented by the equation:

${L_{X} = {\sum\limits_{i}{{x_{i} - x_{i^{\prime}}}}^{N}}},{x_{i} \in X},{x_{i}^{\prime} \in X^{\prime}},{N \in \left\{ {1,2} \right\}},$

where x_(i) represents the i^(th) bit in message X, x′_(i) representsthe i^(th) bit in message X′, and N represents a power to apply incalculating the loss for each combination of x_(i) and x′_(i) (e.g.,linear loss or squared loss).

Hidden message loss L_(H) may be represented by the equation:

${L_{H} = {\sum\limits_{i}{{h_{i} - h_{i}^{\prime}}}^{N}}},{h_{i} \in H},{h_{i}^{\prime} \in H^{\prime}},{N \in \left\{ {1,2} \right\}},$

where h_(i) represents the i^(th) bit in message H, h′_(i) representsthe i^(th) bit in message H′, and N represents a power to apply incalculating the loss for each combination of h_(i) and h′_(i) (e.g.,linear loss or squared loss).

Emulation message loss L_(E) may be represented by the equation:

${L_{E} = {\sum\limits_{i}{{e_{i} - m_{i}^{K}}}^{N}}},{e_{i} \in E},{m_{i}^{K} \in M^{K}},{N \in \left\{ {1_{,}2} \right\}},$

where

${M^{K} = {\underset{M^{k} \in M}{argmin}{{E - M^{K}}}}},{M = \left\{ {M^{0},M^{1},\ldots} \right\}}$

represents the set of legitimate messages that can be transmitted, e_(i)represents the i^(th) bit in the emulation message E, m_(i) representsthe i^(th) bit in message M (which may be a message from the set oflegitimate messages M^(K)), N represents a power to apply in calculatingthe loss for each combination of e_(i) and m_(i) (e.g., linear loss orsquared loss), and K represents an index of a message M^(K) in the setof legitimate messages.

Finally, public performance loss L_(P) may be represented by theequation:

${L_{P} = {\sum\limits_{i}{\alpha_{i}{{q_{i} - p_{i}}}^{N}}}},$

where p_(i) is the i^(th) ideal performance metric, q_(i) is the i^(th)predicted performance metric, a_(i) is the hyperparameter weight for thei^(th) performance metric, and N represents a power to apply incalculating the loss.

The total loss function to be minimized may be represented by theequation:

L=λ _(X) L _(X)+λ_(H) L _(H)+λ_(E) L _(E)+λ_(p) L _(p),

where λ corresponds to a hyperparameter for a respective loss term.

The entropy of message X may be represented by the equation:

${{Entropy}(X)} = {- {\sum\limits_{i}{{p\left( x_{i} \right)}\log{p\left( x_{i} \right)}}}}$

where p(x_(i)) represents the probability of the i^(th) bit field havinga specific value (i.e., 0 or 1). To entropy encode the combined messageinto emulation message E, entropy encoder network 120 can minimize aloss of information by minimizing a divergence between p(X) and p(E),conditioned on the supplemental information S. In some aspects, X and Emay be considered stochastic, while H may be considered non-stochastic.

Self-supervised learning may be used to train entropy encoder network120 to learn the stochasticity of the individual bit fields of firstmessage X as well as the stochasticity of conditional or combinatorialbits of X. A cross-correlation matrix 220, as illustrated in FIG. 2 ,may illustrate the conditional stochasticity of different bits inmessage 210. Generally, in cross-correlation matrix 220, non-zeroentries may indicate a correlation between different bits in the message210, while zeroed entries may indicate a lack of correlation betweendifferent bits in the message 210.

As illustrated, first message 210 includes bit fields x0 through xr,where r+1 represents the number of fields included in first message 210,and cross-correlation matrix 220 may be sized r+1 by r+1 to allow forthe creation of a matrix showing the correlation between different bitsin the first message 210. Generally, the diagonal may be blanked, as acorrelation between a given bit field and itself may not have anymeaning. As discussed, non-zero values may indicate some correlationbetween bits in the message 210. These correlations may exist for bothcombinations of two bits p and q (i.e., illustrating that the value of pis correlated with the value of q, and similarly, the value of q iscorrelated with the value of p.

In some aspects, correlation metrics may be further learned based on thesupplemental information S that may be combined with first message X andsecond message H. By using the supplemental information S, furthercorrelation metrics may be learned based on the conditions associatedwith different signal quality and/or channel quality information. Forexample, when the reported signal quality and/or channel quality metricsindicate a sufficiently high signal quality or stable radio conditionsfor transmission and reception of data, some bit fields may become moreor less relevant to receiver performance or may remain unchanged. Fieldsthat are less relevant to receiver performance, and fields that mayremain unchanged, may thus be considered replaceable fields in which asecond message H may be hidden or otherwise included in message X.

FIG. 3 illustrates an example feedback loop 300 for training andrefining an encoder-decoder neural network (e.g., entropy encodernetwork 120 and entropy decoder network 140 illustrated in FIG. 1 ) usedin generating and decoding emulation messages, according to aspects ofthe present disclosure. While feedback loop 300 omits the PHY layer 130illustrated in FIG. 1 for convenience of illustration, it should beunderstood that the PHY layer 130 is typically interposed betweenentropy encoder network 120 and entropy decoder network 140 to allow fortransmission and reception of emulation messages in a wirelesscommunications system. To generate messages that appear to be validmessages and can be decoded into valid messages using a standarddecoder, the encoder-decoder neural network may be structured as agenerative adversarial network (GAN) that continually learns to betteremulate messages X while including second message H in the emulationmessage E by evaluating the approximations of message X recovered byentropy decoder network 140 and decoder 150. Generally, a GAN may use anadversarial module including a discriminator 310 and a classifier 320 togenerate feedback for entropy encoder network 120 and entropy decodernetwork 140 that continually improves the performance of entropy encodernetwork 120 and entropy decoder network 140.

Generally, discriminator 310 evaluates X′ recovered by the entropydecoder network 140 and X″ recovered by the decoder 150 and generates avalue indicating an amount of difference between X′ and X″. A classifier320 can use the output of the discriminator 310 to determine whether theentropy encoder network 120 and entropy decoder network 140 were able togenerate a message that was sufficiently close to a real message suchthat the recovered approximations X′ and X″ are sufficiently similar.For example, classifier 320 may use thresholding techniques to classifythe performance of the entropy encoder network 120 and entropy decodernetwork 140: difference values or other similarity metrics calculated bydiscriminator 310 having values below a threshold value may beconsidered indicative of sufficiently similar system performance, whiledifference values or other similarity metrics calculated bydiscriminator 310 having values above the threshold may be consideredindicative of different system performance.

If the recovered approximations X′ and X″ are classified as sufficientlysimilar, then model parameters used by entropy encoder network 120 andentropy decoder network 140 need not be updated to further improve theperformance of the entropy encoder network 120 and the entropy decodernetwork 140. If, however, classifier 320 determines that the differencebetween X′ and X″ is greater than a similarity threshold, thenclassifier 320 can determine that entropy encoder network 120 andentropy decoder network 140 are to be updated to better emulate amessage X. The feedback generated by the classifier 320 may include, forexample, information identifying an amount of difference between X′ andX″ and other information that the entropy encoder network 120 andentropy decoder network 140 can use for refining these networks.Generally, X″ may be considered ground truth data, and entropy encodernetwork 120 and entropy decoder network 140 can use this ground truthinformation to refine the networks such that an emulation message isgenerated and decoded by entropy encoder network 120 and entropy decodernetwork 140 into a value closer to that of X″.

FIG. 4 illustrates example operations 400 for generating emulationmessages including a first message and a hidden second message,according to aspects of the present disclosure. Operations 400 may beperformed, for example, by a transmitting device, such as a userequipment (UE) or other network entity (e.g., processing system 800illustrated in FIG. 8 ) that can generate and transmit messages in awireless communications network.

As illustrated, operations 400 may begin at block 410, where a firstmessage and a second message are received. The first message maycorrespond to a legacy message, designated X (e.g., as illustrated inFIG. 1 ), and the second message may correspond to a non-legacy message,designated H (e.g., as illustrated in FIG. 1 ), to be included (andhidden) within the first message. The non-legacy message may includeinformation usable by devices supporting other wireless communicationsstandards, information for enabling device-manufacturer-specificfunctionality, or the like. In some aspects, additional information thatcan be used in generating realistic emulation messages, designated S(e.g., as illustrated in FIG. 1 ) may also be received.

At block 420, the first message X and the second message H are combinedinto a combined message (e.g., by message combiner 110 illustrated inFIG. 1 ). In some aspects, the combined message may further includesupplemental information S that may be used by an encoder neural networkto encode the first message X and the second message H into an emulationmessage E which resembles the first message. This supplementalinformation S may include, for example, historical signal strengthinformation (e.g., RSRQ, RSSI, etc.), channel quality information (CQI,MCS, etc.), or other information that may affect the values carried inthe first message X and thus the values of the emulation message E thatresembles the first message X.

At block 430, an emulation message is generated through an encoderneural network (e.g., entropy encoder network 120 illustrated in FIG. 1) based on the combined message. As discussed, the emulation message Emay be generated such that E, when decoded by a standard decoder (e.g.,a non-neural-network-based decoder), results in an approximation X′ ofthe first message X, where X′=X. In some aspects, the emulation messagemay further be based on a cross-correlation matrix defining correlationsbetween valid bit values in the first message. These correlations may,for example, indicate which bits have values that are correlated withvalues of other bits, such that, for two bits p and q, the value of p iscorrelated with the value of q, and the value of q is also correlatedwith the value of p.

At block 440, the emulation message is output for transmission to areceiving device. Generally, in outputting the emulation message fortransmission, the emulation message may be output to one or morephysical layer components of a wireless communications device in orderfor forward error correction codes (e.g., cyclic redundancy checks orthe like) to be applied to the emulation message prior to transmission.Generally, the application of FEC (e.g., through the FEC encoder blockof PHY layer 130 illustrated in FIG. 1 ) may ensure that the emulationmessage is received with minimal errors, such that E as transmitted bythe transmitting device equals E′ recovered through an FEC decoder atthe receiving device.

FIG. 5 illustrates example operations for receiving and processingemulation messages using a decoder neural network, according to aspectsof the present disclosure. Operations 500 may be performed, for example,by a receiving device, such as a user equipment (UE) or other networkentity (e.g., processing system 800 illustrated in FIG. 8 ) that canreceive and process messages in a wireless communications network.

As illustrated, operations 500 may begin at block 510, where anemulation message E is received for processing. The emulation message Emay comprise a message decodable by a receiver into a first message X.

At block 520, the emulation message is decoded through a decoder neuralnetwork, such as entropy decoder network 140 illustrated in FIG. 1 intoan approximation X′ of the first message X and an approximation H′ of asecond message H. The second message H may generally be a secret messageincluded inside the emulation message E (which may appear to be a validfirst message X to legacy devices). In some aspects, the first messagemay correspond to a legacy message, and the second message maycorrespond to a non-legacy message to be included along with the firstmessage (e.g., as a hidden message inside an emulation message E thatappears to be a valid first message X to legacy devices). The non-legacymessage may include information usable by devices supporting otherwireless communications standards, information for enablingdevice-manufacturer-specific functionality, or the like.

At block 530, one or more actions are taken based on the approximationX′ of the first message X and, in some cases, the approximation H′ ofthe second message H. In some aspects, the actions taken based on theapproximation of the first message may be those defined by a wirelesscommunications standard for such messages. Meanwhile, the actions takenbased on the approximation of the second message may be definedaccording to the content of the second message. For example, the actionstaken based on the approximation of the second message may be actionsassociated with a different wireless communications standard, actionsassociated with device-manufacturer-specific functionality, or the like.

In some aspects, an encoder neural network (e.g., entropy encodernetwork 120) and the decoder neural network (e.g., entropy decodernetwork 140) may be updated based on self-supervision. In order toupdate the encoder neural network and the decoder neural network basedon self-supervision, the emulation message may be decoded by a standard(non-neural-network-based) decoder (e.g. decoder 150), and a differencemay be determined between the decoded emulation message and theapproximation of the first message (e.g., through discriminator 310 andperformance classifier 320 illustrated in FIG. 3 ). A classification ofthe performance of the encoder neural network and the decoder neuralnetwork may be generated based on the determined difference, and theencoder neural network and decoder neural network may be updated basedon the classification of the performance of the encoder and decoderneural networks.

In some aspects, the classification of the performance of the encoderand decoder neural networks may be generated based on a binaryclassifier. The binary classifier may be configured to classify theemulation message as a message equivalent to the approximation of thefirst message or a message different from the approximation of the firstmessage. In some aspects, the classification may be based on adifference between the version of the first message X′ recovered by thedecoder neural network and the version of the first message X″ recoveredby the non-neural-network-based decoder.

FIG. 6 illustrates example operations 600 that may be performed tocommunicate in a wireless communications system via emulation messagesgenerated by an encoder-decoder neural network and train theencoder-decoder neural network based on a determined difference betweenmessages recovered by a decoder in the encoder-decoder neural networkand standard decoder, according to aspects of the present disclosure.Operations 600 may be performed, for example, by a receiving device,such as a user equipment (UE) or other network entity (e.g., processingsystem 800 illustrated in FIG. 8 ) that can generate, transmit, receive,and process messages in a wireless communications network.

As illustrated, operations 600 begin at block 610, where a first messageX and a second message H are combined into a combined message. Asdiscussed, the first message X may be a legacy message, and the secondmessage H may be a non-legacy message (e.g., associated with a newversion of a wireless communications standard, with a different wirelesscommunications standard, with device-manufacturer-specificfunctionality, etc.).

At block 620, an emulation message E is generated through an encoderneural network, such as entropy encoder network 120 illustrated in FIG.3 , based on the combined message. The emulation message generallycomprises a message decodable by a receiver into the first message (oran approximation thereof). As discussed above, the encoder neuralnetwork may be trained based on a cross-correlation matrix definingcorrelations between valid bit values in the first message and, in someaspects, further based on correlations between valid bit values based onone or more of signal strength information or channel qualityinformation.

At block 630, the emulation message is decoded, through a decoder neuralnetwork, such as entropy decoder network 140 illustrated in FIG. 3 ,into an approximation of the first message and an approximation of thesecond message.

At block 640, one or more actions are taken based on the approximationof the first message and, in some cases, the approximation of the secondmessage. These actions may include processing the first messageaccording to rules defined by a wireless communications standard, and/orperforming one or more device-manufacturer-specific actions based on theapproximation of the second message.

At block 650, the encoder neural network and decoder neural network areretrained based on a difference between the approximation X′ of thefirst message generated by the decoder neural network and theapproximation X″ of the first message generated by decoding E using astandard decoder. In this example, at least one of the encoder neuralnetwork or the decoder neural network may comprise a generativeadversarial network. In some aspects, the generative adversarial networkmay be updated based on self-supervision through a discriminator (e.g.,discriminator 310 illustrated in FIG. 3 ) and classifier (e.g.,performance classifier 320 illustrated in FIG. 3 ) that examines thedecoded emulation message (e.g., decoded through anon-neural-network-based decoder) and the approximation of the firstmessage. Based on a difference between the decoded emulation message andthe approximation of the first message, the performance of the encoderneural network and the decoder neural network may be classified, and theencoder and decoder neural network may be updated based on theclassification of the emulation message.

FIG. 7 illustrates an example message space in which an encoder-decoderneural network is trained to generate emulation messages E for a firstmessage X and second message H, according to aspects of the presentdisclosure. As illustrated, message space 700 includes three spaces:space 710 corresponding to the universe of all legitimate messages thatmay be generated for a given message with bit size n; space 720corresponding to realistic messages that may be generated within awireless communications network (e.g., due to correlations betweenvalues of certain fields in the message and channel quality information,signal strength information, etc.); and space 730 corresponding to theemulation messages E that may be generated by an encoder-decoder neuralnetwork.

In some aspects, M may be the universe of downlink control information(DCI) messages that may be transmitted in a wireless communicationsnetwork. While M may cover a large number of combinations of bit values,a subset of those combinations of bit values may be realistic. Forexample, it may be realistic for a DCI message to include high channelquality information (CQI) values when the supplemental information Sindicates high reception quality, but may not be realistic for the DCImessage to include low CQI values in such a case. Therefore, space 720representing the universe of messages that are realistic may be smallerthan space 710 representing the universe of possible messages in M.

An entropy encoder network may generate messages within space 730, whichincludes a portion of space 720 representing the universe of realisticmessages and a portion of space 710 representing the universe of allmessages. Emulation messages E may not solely reside in space 720, asunused or irrelevant bit fields in realistic messages may be used, inconjunction with other bits in messages from space 710, to embed orotherwise include a second message H in an emulation message.

Generally, the set of messages of ∥X∥ bits may have 2^(∥X∥) degrees offreedom. An entropy encoder, as discussed herein, may take ∥X∥+∥H∥ bitsand encode those bits into ∥E∥, where ∥X∥+∥H∥>∥E∥. In some aspects, when∥H∥0, the channel conditions and other supplemental information S may besuch that a message H may not be included in the emulation messagewithout degrading the performance of a system. For example, when channelconditions change significantly, meaning that many bits in a message mayhave a different value and meaning from those in preceding messages,there may be an insufficient number of bits available for immersingmessage H into message X and delivering the combination of X and H in anemulation message E that would decode to X.

Example Processing Systems for Encoding and Decoding Emulation MessagesUsing Encoder-Decoder Neural Networks

FIG. 8 depicts an example processing system 800 for transmitting andreceiving emulation messages generated (encoded) and decoded using anencoder-decoder neural network, such as described herein for examplewith respect to FIGS. 4 through 6 .

Processing system 800 includes a central processing unit (CPU) 802,which in some examples may be a multi-core CPU. Instructions executed atthe CPU 802 may be loaded, for example, from a program memory associatedwith the CPU 802 or may be loaded from a memory 824.

Processing system 800 also includes additional processing componentstailored to specific functions, such as a graphics processing unit (GPU)804, a digital signal processor (DSP) 806, a neural processing unit(NPU) 808, a multimedia processing unit 810, a wireless connectivitycomponent 812.

An NPU, such as NPU 808, is generally a specialized circuit configuredfor implementing the control and arithmetic logic for executing machinelearning algorithms, such as algorithms for processing artificial neuralnetworks (ANNs), deep neural networks (DNNs), random forests (RFs), andthe like. An NPU may sometimes alternatively be referred to as a neuralsignal processor (NSP), tensor processing unit (TPU), neural networkprocessor (NNP), intelligence processing unit (IPU), vision processingunit (VPU), or graph processing unit.

NPUs, such as NPU 808, are configured to accelerate the performance ofcommon machine learning tasks, such as image classification, machinetranslation, object detection, and various other predictive models. Insome examples, a plurality of NPUs may be instantiated on a single chip,such as a system on a chip (SoC), while in other examples they may bepart of a dedicated neural-network accelerator.

NPUs may be optimized for training or inference, or in some casesconfigured to balance performance between both. For NPUs that arecapable of performing both training and inference, the two tasks maystill generally be performed independently.

NPUs designed to accelerate training are generally configured toaccelerate the optimization of new models, which is a highlycompute-intensive operation that involves inputting an existing dataset(often labeled or tagged), iterating over the dataset, and thenadjusting model parameters, such as weights and biases, in order toimprove model performance. Generally, optimizing based on a wrongprediction involves propagating back through the layers of the model anddetermining gradients to reduce the prediction error.

NPUs designed to accelerate inference are generally configured tooperate on complete models. Such NPUs may thus be configured to input anew piece of data and rapidly process this piece through an alreadytrained model to generate a model output (e.g., an inference).

In one implementation, NPU 808 is a part of one or more of CPU 802, GPU804, and/or DSP 806.

In some examples, wireless connectivity component 812 may includesubcomponents, for example, for third generation (3G) connectivity,fourth generation (4G) connectivity (e.g., 4G LTE), fifth generationconnectivity (e.g., 5G or NR), Wi-Fi connectivity, Bluetoothconnectivity, and other wireless data transmission standards. Wirelessconnectivity component 812 is further coupled to one or more antennas814.

In some examples, one or more of the processors of processing system 800may be based on an ARM or RISC-V instruction set.

Processing system 800 also includes memory 824, which is representativeof one or more static and/or dynamic memories, such as a dynamic randomaccess memory, a flash-based static memory, and the like. In thisexample, memory 824 includes computer-executable components, which maybe executed by one or more of the aforementioned processors ofprocessing system 800.

In particular, in this example, memory 824 includes message receivingcomponent 824A, message combining component 824B, emulation messagegenerating component 824C, message outputting component 824D, emulationmessage decoding component 824E, action taking component 824F,encoder-decoder neural network component 824G, and encoder-decoderretraining component 824H. The depicted components, and others notdepicted, may be configured to perform various aspects of the methodsdescribed herein.

Generally, processing system 800 and/or components thereof may beconfigured to perform the methods described herein.

Notably, in other aspects of the present disclosure, elements ofprocessing system 800 may be omitted, such as where processing system800 is a server computer or the like. For example, multimedia processingunit 810, wireless connectivity component 812, sensors 816, ISPs 818,and/or navigation component 820 may be omitted in other aspects of thepresent disclosure. Further, elements of processing system 800 may bedistributed, such as distributing, amongst various components of adistributed computing environment, features related to training a modeland features related to using the model to generate inferences.

Example Clauses

Implementation details of various aspects of the present disclosure arepresented in the following numbered clauses.

Clause 1: A processor-implemented method, comprising: receiving a firstmessage and a second message, wherein the second message comprises asecret message to be hidden inside the first message; combining thefirst message and the second message into a combined message;generating, through an encoder neural network, an emulation messagebased on the combined message, wherein the emulation message comprises amessage decodable by a receiving device into the first message; andoutputting the emulation message for transmission to the receivingdevice.

Clause 2: The method of Clause 1, wherein generating the emulationmessage is further based on supplemental information.

Clause 3: The method of Clause 2, wherein the supplemental informationcomprises a historical signal strength information.

Clause 4: The method of Clause 2 or 3, wherein the supplementalinformation comprises historical channel quality information.

Clause 5: The method of any of Clauses 1 through 4, wherein the encoderneural network comprises a neural network trained to generate theemulation message based on a cross-correlation matrix definingcorrelations between valid bit values in the first message.

Clause 6: The method of any of Clauses 1 through 5, wherein the firstmessage comprises a legacy message and wherein the second messagecomprises a non-legacy message embedded into the legacy message.

Clause 7: A processor-implemented method, comprising: receiving anemulation message for processing, wherein the emulation messagecomprises a message decodable by a receiver into a first message;decoding, through a decoder neural network, the emulation message intoan approximation of the first message and an approximation of a secondmessage, the second message comprising a secret message hidden withinthe first message; and taking one or more actions based on theapproximation of the first message and the approximation of the secondmessage.

Clause 8: The method of Clause 7, wherein the first message comprises alegacy message and wherein the second message comprises a non-legacymessage embedded into the legacy message.

Clause 9: The method of any Clause 7 or 8, further comprising updatingan encoder neural network and the decoder neural network based onself-supervision.

Clause 10: The method of Clause 9, wherein updating the encoder neuralnetwork and the decoder neural network based on self-supervisioncomprises: decoding the emulation message through a decoder differentfrom the decoder neural network; determining a difference between thedecoded emulation message and the approximation of the first message;generating a classification of performance of the encoder neural networkand the decoder neural network based on the determined difference; andupdating the encoder neural network and the decoder neural network basedon the classification of the performance of the encoder neural networkand the decoder neural network.

Clause 11: The method of Clause 10, wherein generating theclassification of the performance of the encoder neural network and thedecoder neural network comprises generating the classification based ona binary classifier configured to classify the emulation message as amessage equivalent to the approximation of the first message or amessage different from the approximation of the first message.

Clause 12: A processor-implemented method, comprising: combining a firstmessage and a second message into a combined message; generating,through an encoder neural network, an emulation message based on thecombined message, wherein the emulation message comprises a messagedecodable by a receiver into the first message; decoding, through adecoder neural network, the emulation message into an approximation ofthe first message and an approximation of the second message; and takingone or more actions based on the approximation of the first message andthe approximation of the second message.

Clause 13: The method of Clause 12, wherein the encoder neural networkand the decoder neural network each comprise a generative adversarialnetwork.

Clause 14: The method of Clause 12 or 13, wherein the encoder neuralnetwork is trained to generate the emulation message based on minimizinga carrier message loss function, a hidden message loss function, and anemulation message loss function.

Clause 15: The method of any of Clauses 12 through 14, wherein theencoder neural network is further configured to generate the emulationmessage based on supplemental information.

Clause 16: The method of Clause 15, wherein the supplemental informationcomprises a historical signal strength information.

Clause 17: The method of Clause 15 or 16, wherein the supplementalinformation comprises historical channel quality information.

Clause 18: The method of any of Clauses 12 through 17, furthercomprising updating the encoder neural network and the decoder neuralnetwork based on self-supervision.

Clause 19: The method of Clause 18, wherein updating the encoder neuralnetwork and the decoder neural network based on self-supervisioncomprises: decoding the emulation message through a decoder differentfrom the decoder neural network; determining a difference between thedecoded emulation message and the approximation of the first message;generating a classification of performance of the encoder neural networkand the decoder neural network based on the determined difference; andupdating the encoder neural network and the decoder neural network basedon the classification of the performance of the encoder neural networkand the decoder neural network.

Clause 20: The method of Clause 19, wherein generating theclassification of the performance of the encoder neural network and thedecoder neural network comprises generating the classification based ona binary classifier configured to classify the emulation message as amessage equivalent to the approximation of the first message or amessage different from the approximation of the first message.

Clause 21: The method of any of Clauses 12 through 22, wherein theencoder neural network is trained based on a cross-correlation matrixdefining correlations between valid bit values in the first message.

Clause 22: The method of Clause 21, wherein the cross-correlation matrixfurther defines correlations between valid bit values in the firstmessage based on one or more of signal strength information or channelquality information.

Clause 23: The method of any of Clauses 12 through 22, wherein the firstmessage comprises a legacy message and wherein the second messagecomprises a non-legacy message embedded into the legacy message.

Clause 24: A processing system comprising: a memory comprisingcomputer-executable instructions; and one or more processors configuredto execute the computer-executable instructions and cause the processingsystem to perform a method in accordance with any of Clauses 1-23.

Clause 25: A processing system comprising means for performing a methodin accordance with any of Clauses 1-23.

Clause 26: A non-transitory computer-readable medium comprisingcomputer-executable instructions that, when executed by one or moreprocessors of a processing system, cause the processing system toperform a method in accordance with any of Clauses 1-23.

Clause 27: A computer program product embodied on a computer-readablestorage medium comprising code for performing a method in accordancewith any of Clauses 1-23.

Additional Considerations

The preceding description is provided to enable any person skilled inthe art to practice the various aspects described herein. The examplesdiscussed herein are not limiting of the scope, applicability, oraspects set forth in the claims. Various modifications to these aspectswill be readily apparent to those skilled in the art, and the genericprinciples defined herein may be applied to other aspects. For example,changes may be made in the function and arrangement of elementsdiscussed without departing from the scope of the disclosure. Variousexamples may omit, substitute, or add various procedures or componentsas appropriate. For instance, the methods described may be performed inan order different from that described, and various steps may be added,omitted, or combined. Also, features described with respect to someexamples may be combined in some other examples. For example, anapparatus may be implemented or a method may be practiced using anynumber of the aspects set forth herein. In addition, the scope of thedisclosure is intended to cover such an apparatus or method that ispracticed using other structure, functionality, or structure andfunctionality in addition to, or other than, the various aspects of thedisclosure set forth herein. It should be understood that any aspect ofthe disclosure disclosed herein may be embodied by one or more elementsof a claim.

As used herein, the word “exemplary” means “serving as an example,instance, or illustration.” Any aspect described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother aspects.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover a, b, c,a-b, a-c, b-c, and a-b-c, as well as any combination with multiples ofthe same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b,b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining, and thelike. Also, “determining” may include receiving (e.g., receivinginformation), accessing (e.g., accessing data in a memory), and thelike. Also, “determining” may include resolving, selecting, choosing,establishing, and the like.

The methods disclosed herein comprise one or more steps or actions forachieving the methods. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims. Further, thevarious operations of methods described above may be performed by anysuitable means capable of performing the corresponding functions. Themeans may include various hardware and/or software component(s) and/ormodule(s), including, but not limited to a circuit, an applicationspecific integrated circuit (ASIC), or processor. Generally, where thereare operations illustrated in figures, those operations may havecorresponding counterpart means-plus-function components with similarnumbering.

The following claims are not intended to be limited to the aspects shownherein, but are to be accorded the full scope consistent with thelanguage of the claims. Within a claim, reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.” Unless specifically statedotherwise, the term “some” refers to one or more. No claim element is tobe construed under the provisions of 35 U.S.C. § 112(f) unless theelement is expressly recited using the phrase “means for” or, in thecase of a method claim, the element is recited using the phrase “stepfor.” All structural and functional equivalents to the elements of thevarious aspects described throughout this disclosure that are known orlater come to be known to those of ordinary skill in the art areexpressly incorporated herein by reference and are intended to beencompassed by the claims. Moreover, nothing disclosed herein isintended to be dedicated to the public regardless of whether suchdisclosure is explicitly recited in the claims.

What is claimed is:
 1. A processor-implemented method, comprising:receiving a first message and a second message, wherein the secondmessage comprises a secret message to be embedded in the first message;combining the first message and the second message into a combinedmessage; generating, through an encoder neural network, an emulationmessage based on the combined message, wherein the emulation messagecomprises a message decodable into the first message; and outputting theemulation message for transmission to a receiving device.
 2. The methodof claim 1, wherein generating the emulation message is further based onsupplemental information.
 3. The method of claim 2, wherein thesupplemental information comprises historical signal strengthinformation.
 4. The method of claim 2, wherein the supplementalinformation comprises historical channel quality information.
 5. Themethod of claim 1, wherein the encoder neural network comprises a neuralnetwork trained to generate the emulation message based on across-correlation matrix defining correlations between valid bit valuesin the first message.
 6. The method of claim 1, wherein the firstmessage comprises a legacy message and wherein the second messagecomprises a non-legacy message embedded in the legacy message.
 7. Aprocessor-implemented method, comprising: receiving an emulation messagefor processing, wherein the emulation message comprises a messagedecodable by a receiver into a first message; decoding, through adecoder neural network, the emulation message into an approximation ofthe first message and an approximation of a second message, the secondmessage comprising a secret message hidden within the first message; andtaking one or more actions based on the approximation of the firstmessage and the approximation of the second message.
 8. The method ofclaim 7, wherein the first message comprises a legacy message andwherein the second message comprises a non-legacy message embedded inthe legacy message.
 9. The method of claim 7, further comprisingupdating an encoder neural network and the decoder neural network basedon self-supervision.
 10. The method of claim 9, wherein updating theencoder neural network and the decoder neural network based onself-supervision comprises: decoding the emulation message through adecoder different from the decoder neural network; determining adifference between the decoded emulation message and the approximationof the first message; generating a classification of performance of theencoder neural network and the decoder neural network based on thedetermined difference; and updating the encoder neural network and thedecoder neural network based on the classification of the performance ofthe encoder neural network and the decoder neural network.
 11. Themethod of claim 10, wherein generating the classification of theperformance of the encoder neural network and the decoder neural networkcomprises generating the classification based on a binary classifierconfigured to classify the emulation message as a message equivalent tothe approximation of the first message or a message different from theapproximation of the first message.
 12. A processor-implemented method,comprising: combining a first message and a second message into acombined message; generating, through an encoder neural network, anemulation message based on the combined message, wherein the emulationmessage comprises a message decodable by a receiver into the firstmessage; decoding, through a decoder neural network, the emulationmessage into an approximation of the first message and an approximationof the second message; and taking one or more actions based on theapproximation of the first message and the approximation of the secondmessage.
 13. The method of claim 12, wherein the encoder neural networkand the decoder neural network each comprise a generative adversarialnetwork.
 14. The method of claim 12, wherein the encoder neural networkis trained to generate the emulation message based on minimizing acarrier message loss function, a hidden message loss function, and anemulation message loss function.
 15. The method of claim 12, wherein theencoder neural network is further configured to generate the emulationmessage based on supplemental information.
 16. The method of claim 15,wherein the supplemental information comprises historical signalstrength information.
 17. The method of claim 15, wherein thesupplemental information comprises historical channel qualityinformation.
 18. The method of claim 12, further comprising updating theencoder neural network and the decoder neural network based onself-supervision.
 19. The method of claim 18, wherein updating theencoder neural network and the decoder neural network based onself-supervision comprises: decoding the emulation message through adecoder different from the decoder neural network; determining adifference between the decoded emulation message and the approximationof the first message; generating a classification of performance of theencoder neural network and the decoder neural network based on thedetermined difference; and updating the encoder neural network and thedecoder neural network based on the classification of the performance ofthe encoder neural network and the decoder neural network.
 20. Themethod of claim 19, wherein generating the classification of theperformance of the encoder neural network and the decoder neural networkcomprises generating the classification based on a binary classifierconfigured to classify the emulation message as a message equivalent tothe approximation of the first message or a message different from theapproximation of the first message.
 21. The method of claim 12, whereinthe encoder neural network is trained based on a cross-correlationmatrix defining correlations between valid bit values in the firstmessage.
 22. The method of claim 21, wherein the cross-correlationmatrix further defines correlations between valid bit values in thefirst message based on one or more of signal strength information orchannel quality information.
 23. The method of claim 12, wherein thefirst message comprises a legacy message and wherein the second messagecomprises a non-legacy message embedded into the legacy message.
 24. Asystem, comprising: a memory having executable instructions storedthereon; and a processor configured to execute the executableinstructions in order to cause the system to: receive a first messageand a second message, wherein the second message comprises a secretmessage to be embedded in the first message; combine the first messageand the second message into a combined message; generate, through anencoder neural network, an emulation message based on the combinedmessage, wherein the emulation message comprises a message decodable bya receiving device into the first message; and output the emulationmessage for transmission to the receiving device.
 25. The system ofclaim 24, wherein the processor is configured to execute the executableinstructions in order to cause the system to generate the emulationmessage further based on supplemental information.
 26. The system ofclaim 25, wherein the supplemental information comprises historicalsignal strength information.
 27. The system of claim 25, wherein thesupplemental information comprises historical channel qualityinformation.
 28. The system of claim 24, wherein the encoder neuralnetwork comprises a neural network trained to generate the emulationmessage based on a cross-correlation matrix defining correlationsbetween valid bit values in the first message.
 29. The system of claim24, wherein the first message comprises a legacy message and wherein thesecond message comprises a non-legacy message embedded in the legacymessage.